Analyte Monitoring Data Processing With Child-Process Segmentation
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Solution Overview
Problem
Existing methods for processing large volumes of analyte data, such as glucose data from continuous glucose monitors, are slow and inefficient, leading to unacceptable delays in interactive user interfaces.
Innovation Solution
Implementing multiprocessing techniques to process analyte data using multiple child processes that aggregate results, reducing processing time from minutes to seconds while avoiding concurrent processing of the same data entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If single-process data processing is used, then processing simplicity is maintained, but processing speed becomes unacceptably slow
Solution Approach 1:
The patent divides the analyte data processing task into multiple child processes, each handling a specific subset of data entries. The parent process spawns child processes that independently process different time windows or data ranges, then aggregates their results. This segmentation enables parallel processing of large volumes of analyte data, dramatically improving processing speed while maintaining manageable complexity through clear process hierarchy and division of labor.
2Productivity
If multiple processes are used to speed up processing, then processing speed improves, but risk of concurrent processing of same data entries increases
Solution Approach 1:
The patent segments the analyte data into distinct subsets assigned to different child processes, with each process handling a specific time window or data range. This segmentation ensures that concurrent processes work on disjoint data portions, eliminating the risk of duplicate processing while maximizing parallel throughput. The parent process coordinates the segmentation and aggregation to maintain both productivity and reliability.
3Quantity of substance
If large volumes of analyte data are processed, then comprehensive analysis is achieved, but processing time becomes unacceptably long
Solution Approach 1:
The patent segments large volumes of analyte data into smaller chunks that can be processed in parallel by multiple child processes. Each child process handles a specific subset of data entries within a defined time window, allowing comprehensive analysis of the entire dataset while reducing overall processing time through concurrent execution. The results from all child processes are then aggregated by the parent process to produce the complete analysis.
Solution Approach 2:
The patent performs preliminary actions by pre-defining data subsets and time windows before spawning child processes. The parent process prepares the data segmentation scheme and assigns specific ranges to each child process in advance, enabling immediate parallel processing without coordination overhead during execution. This preliminary organization optimizes both the volume of data that can be processed and the time required to complete the analysis.
Data Source
AI summary
Systems and methods are provided for improved analyte processing with data that was captured by analyte monitors. Analyte data entries are processed with multiple child processes and the child processes pass results to a parent process. The parent process aggregates the children results to result in faster processing times. The analyte data is processed in a backend system that is linked to user computing devices with graphical user interfaces.